Part One: Bored Ape Yatch Club¶

@angel-estrada7

Bored Ape Art

In [2]:
# Import dependancies
import os
import requests
import pandas as pd
import json
from dotenv import load_dotenv
from etherscan_py import etherscan_py
import plotly.express as px

import matplotlib.pyplot as plt 
import hvplot.pandas
import numpy as np
import datetime as dt
import seaborn as sns
from pathlib import Path

%matplotlib inline
In [3]:
# Loading .env containing our keys
load_dotenv()
Out[3]:
True
In [4]:
# create variable for api key 
api_key = os.getenv('COVALENT_API_KEY')
type(api_key)
Out[4]:
str
In [5]:
# import dependancy
from etherscan_py import etherscan_py
etherscan_api = etherscan_py.Client(os.getenv('ETHERSCAN_API'))

# Print current eth price
eth_value = etherscan_api.get_eth_price()
eth_value
Out[5]:
2693.29

Set Variables¶

In [6]:
# Append url for our api
url = "https://api.covalenthq.com/v1"
chain_id = "/1"
azuki_address = "/0xED5AF388653567Af2F388E6224dC7C4b3241C544"
cryptopunks_address = "/0xb47e3cd837dDF8e4c57F05d70Ab865de6e193BBB"
BAYC_address = "/0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D"
date_option = '/?quote-currency=USD&format=JSON&from=2017-01-01&to=2022-05-01'
page_option = '/transactions_v2/?quote-currency=USD&format=JSON&block-signed-at-asc=false&no-logs=false&page-number=0&page-size=1000'
api_option = "&key=" + api_key
api_no_option = '/?key=' + api_key

a. Daily Volume¶

In [7]:
# Create variables needed for owner data and add to url
BAYC_historical_url = url + chain_id + "/nft_market/collection" + BAYC_address + api_no_option

# Get request
BAYC_historical_json = requests.get(BAYC_historical_url).json()

# Convert historical json data to a dataframe and view data
BAYC_df = pd.DataFrame(BAYC_historical_json['data']['items'])

# Set index to date
BAYC_df = BAYC_df.set_index('opening_date')

# Create Volume dataframe
BAYC_vol_df = pd.DataFrame(BAYC_df, columns = ['volume_quote_day', 'unique_token_ids_sold_count_day']).sort_index()
BAYC_vol_df.head()
Out[7]:
volume_quote_day unique_token_ids_sold_count_day
opening_date
2021-04-30 8.241964e+02 1
2021-05-01 1.737182e+06 1635
2021-05-02 4.950946e+06 1534
2021-05-03 3.948996e+06 996
2021-05-04 1.388962e+06 336
In [8]:
# Plot Volume quote per day
BAYC_volume = BAYC_vol_df['volume_quote_day'].astype(int)

# BAYC_volume.plot.bar(figsize = (20,4))
px.bar(BAYC_volume)

b. Recent 1000 transactions¶

In [9]:
# Quering the API for transaction data
BAYC_tx_url = url + chain_id + "/address" + BAYC_address + page_option + api_option 
BAYC_tx = requests.get(BAYC_tx_url).json()

# Convert transactions data to dataframe
BAYC_tx_df = pd.DataFrame(BAYC_tx['data']['items'], columns = ['to_address_label','fees_paid', 'value_quote','block_signed_at']).set_index('block_signed_at').sort_index()

BAYC_tx_df.head()
Out[9]:
to_address_label fees_paid value_quote
block_signed_at
2022-05-03T04:58:00Z None 3165483669662600 0.0
2022-05-03T05:03:17Z None 1487241858437776 0.0
2022-05-03T05:05:46Z None 63010705583674080 0.0
2022-05-03T05:17:24Z None 2254442804107520 0.0
2022-05-03T05:18:16Z None 2407384820533160 0.0
In [10]:
# Filter Through data for non null transactions
BAYC_sales_df = BAYC_tx_df[BAYC_tx_df['value_quote'] != 0]
BAYC_sales = BAYC_sales_df[BAYC_sales_df['to_address_label'].notnull()].dropna()

# Creating the plot using plotly express
BAYC_fig = px.bar(BAYC_sales, 
                  x='to_address_label', 
                  y= 'value_quote',
                  color='value_quote',
                  height=1020,
                  width = 1000,
                  barmode = 'overlay',
                  labels={'value_quote':'Amount in USD', 'to_address_label': 'Exchange'},
                  title='USD spent to buy Bore Apes in recent 1000 transactions'
                 )
BAYC_fig.show()

c. Fees Spend¶

In [11]:
# Filter Through data for non null transactions
BAYC_fees = BAYC_sales_df['fees_paid'].astype(int)/10**18*eth_value

BAYC_fees.plot.bar(rot = 90, figsize = (20,5), ylabel = 'value in USD')
Out[11]:
<AxesSubplot:xlabel='block_signed_at', ylabel='value in USD'>

Part TWO: Azuki¶

@mmsaki

Azuki Art

a. Daily Volume¶

In [12]:
# Create variables needed for owner data and add to url
azuki_url = url + chain_id + "/nft_market/collection" + azuki_address + api_no_option

# Get request
azuki_historical_json = requests.get(azuki_url).json()

# Convert historical json data to a dataframe and view data
azuki_df = pd.DataFrame(azuki_historical_json['data']['items'])

# Set index to date
azuki_df = azuki_df.set_index('opening_date')

# Create Volume dataframe
azuki_vol_df = pd.DataFrame(azuki_df, columns = ['volume_quote_day', 'unique_token_ids_sold_count_day']).sort_index()
azuki_vol_df.head()
Out[12]:
volume_quote_day unique_token_ids_sold_count_day
opening_date
2022-01-12 45941404.0 2402
2022-01-13 25129178.0 1318
2022-01-14 168151840.0 470
2022-01-15 4408686.0 499
2022-01-16 295638336.0 368
In [13]:
# Plot Volume quote per day
azuki_volume = azuki_vol_df['volume_quote_day'].astype(int)

# Plot Historical daily volume
px.bar(azuki_volume)

b. Recent 1000 transactions¶

In [14]:
# Quering the API for transaction data
azuki_tx_url = url + chain_id + "/address" + azuki_address + page_option + api_option 
azuki_tx = requests.get(azuki_tx_url).json()

# Convert transactions data to dataframe
azuki_tx_df = pd.DataFrame(azuki_tx['data']['items'], columns = ['to_address_label','fees_paid', 'value_quote','block_signed_at']).set_index('block_signed_at').sort_index()

azuki_tx_df.head()
Out[14]:
to_address_label fees_paid value_quote
block_signed_at
2022-05-03T05:10:50Z None 4127333930314595 0.000000
2022-05-03T05:18:16Z Wyvern Exchange Contract (-) 10140874476295150 90667.575461
2022-05-03T05:18:42Z None 2570300864764061 0.000000
2022-05-03T05:23:58Z None 37351159788905740 0.000000
2022-05-03T05:26:13Z Gem: GemSwap 2 11788059244049616 88137.185303
In [15]:
# Filter Through data for non null transactions
azuki_sales_df = azuki_tx_df[azuki_tx_df['value_quote'] != 0]
azuki_sales = azuki_sales_df[azuki_sales_df['to_address_label'].notnull()]

# Creating the plot using plotly express
azuki_fig = px.bar(azuki_sales, 
                   x='to_address_label',
                   y= 'value_quote',
                   color='value_quote',
                   height=1020,
                   width = 1000,
                   barmode='overlay',
                   labels={'value_quote':'Amount in USD', 'to_address_label': 'Exchange'},
                   title='USD spent to buy Azukis in recent 1000 transactions'
                  )
azuki_fig.show()

c. fees paid¶

In [16]:
# Filter Through data for non null transactions
azuki_fees = azuki_sales_df['fees_paid'].astype(int)/10**18*eth_value

azuki_fees.plot.bar(rot = 90, figsize = (20,5), ylabel = 'value in USD')
Out[16]:
<AxesSubplot:xlabel='block_signed_at', ylabel='value in USD'>

d. comparison¶

In [17]:
# Create variables needed for owner data and append to url
cryptopunks_historical_url = url + chain_id + "/nft_market/collection" + cryptopunks_address + api_no_option

# Get request
cryptopunks_historical_json = requests.get(cryptopunks_historical_url).json()

# Convert historical json data to a dataframe and view data
cryptopunks_df = pd.DataFrame(cryptopunks_historical_json['data']['items'])

# Set index to date
cryptopunks_df = cryptopunks_df.set_index('opening_date')

# Create Volume dataframe
cryptopunks_vol_df = pd.DataFrame(cryptopunks_df, columns = ['volume_quote_day', 'unique_token_ids_sold_count_day']).sort_index()
cryptopunks_vol_df.head()
Out[17]:
volume_quote_day unique_token_ids_sold_count_day
opening_date
2017-06-23 0.0 19
2017-06-24 0.0 22
2017-06-25 0.0 11
2017-06-26 0.0 18
2017-06-27 0.0 35

punk volume a.¶

In [18]:
# Plot Volume quote per day
cryptopunks_volume = cryptopunks_vol_df['volume_quote_day'].astype(int)

# cryptopunks_volume.plot.line(figsize = (20,4))

px.bar(cryptopunks_volume)

punk sales b.¶

In [19]:
# Quering the API for transaction data
cryptopunks_tx_url = url + chain_id + "/address" + cryptopunks_address + page_option + api_option 
cryptopunks_tx = requests.get(cryptopunks_tx_url).json()

# Convert transactions data to dataframe
cryptopunks_tx_df = pd.DataFrame(cryptopunks_tx['data']['items'], columns = ['to_address_label','fees_paid', 'value_quote','block_signed_at']).set_index('block_signed_at').sort_index()

cryptopunks_tx_df.head()
Out[19]:
to_address_label fees_paid value_quote
block_signed_at
2022-04-28T02:07:46Z CRYPTOPUNKS (Ͼ) 8059300000000000 179579.760742
2022-04-28T02:25:04Z CRYPTOPUNKS (Ͼ) 2233845322427811 0.000000
2022-04-28T02:28:31Z CRYPTOPUNKS (Ͼ) 1553815767995790 0.000000
2022-04-28T02:29:01Z CRYPTOPUNKS (Ͼ) 1833778000000000 0.000000
2022-04-28T02:29:56Z None 19024222924419000 0.000000
In [20]:
# Create variables needed for owner data and append to url
cryptopunks_historical_url = url + chain_id + "/nft_market/collection" + cryptopunks_address + api_no_option

# Get request
cryptopunks_historical_json = requests.get(cryptopunks_historical_url).json()

# Convert historical json data to a dataframe and view data
cryptopunks_df = pd.DataFrame(cryptopunks_historical_json['data']['items'])

# Set index to date
cryptopunks_df = cryptopunks_df.set_index('opening_date')

# Create Volume dataframe
cryptopunks_vol_df = pd.DataFrame(cryptopunks_df, columns = ['volume_quote_day', 'unique_token_ids_sold_count_day']).sort_index()
cryptopunks_vol_df.head()
Out[20]:
volume_quote_day unique_token_ids_sold_count_day
opening_date
2017-06-23 0.0 19
2017-06-24 0.0 22
2017-06-25 0.0 11
2017-06-26 0.0 18
2017-06-27 0.0 35
In [21]:
# Filter Through data for non null transactions
cryptopunks_sales_df = cryptopunks_tx_df[cryptopunks_tx_df['value_quote'] != 0]
cryptopunks_sales = cryptopunks_sales_df[cryptopunks_sales_df['to_address_label'].notnull()].dropna()


# Creating the plot using plotly express
cryptopunks_fig = px.bar(cryptopunks_sales, 
                         x='to_address_label', 
                         y= 'value_quote',
                         color='value_quote',
                         height=1020,
                         width = 1000,
                         barmode = 'overlay',
                         labels={'value_quote':'Amount in USD', 'to_address_label': 'Exchange'},
                         title='USD spent to buy Cryptopunks in recent 1000 transactions'
                        )
cryptopunks_fig.show()

punk fees c.¶

In [22]:
# Filter Through data for non null transactions
cryptopunks_fees = cryptopunks_sales_df['fees_paid'].astype(int)/10**18*eth_value

# plot
cryptopunks_fees.plot.bar(rot = 90, figsize = (20,5), ylabel = 'value in USD')
Out[22]:
<AxesSubplot:xlabel='block_signed_at', ylabel='value in USD'>

combined fees d.¶

In [23]:
# Group by address label and sum the value
azuki_total = azuki_sales.groupby('to_address_label').sum()
cryptopunks_total = cryptopunks_sales.groupby('to_address_label').sum()
BAYC_total = BAYC_sales.groupby('to_address_label').sum()
In [24]:
# Combine and rename columns for our total sales data
combined_totals = pd.concat([azuki_total,cryptopunks_total,BAYC_total], axis=1)
combined_totals.columns = ['azuki_total', 'cryptopunks_total','BAYC_total']
In [25]:
# Group by address label and sum the value
combined_totals
Out[25]:
azuki_total cryptopunks_total BAYC_total
to_address_label
Gem: GemSwap 2 9.033323e+05 3.683260e+05 2.568193e+06
LooksRare: Exchange 2.030164e+06 NaN 9.019504e+06
Wyvern Exchange Contract (-) 7.022424e+06 NaN 2.870285e+07
CRYPTOPUNKS (Ͼ) NaN 2.800161e+07 NaN
In [26]:
# Plot for combined figure
combined_total_fig = px.bar(combined_totals, height = 700)


# Show Figure
combined_total_fig.show()

combined fees e.¶

In [27]:
# Combine and rename columns for our total sales data
azuki_usd_fees = azuki_sales['fees_paid'].astype(int)/10**18*eth_value
cryptopunks_usd_fees = cryptopunks_sales['fees_paid'].astype(int)/10**18*eth_value
BAYC_usd_fees = BAYC_sales['fees_paid'].astype(int)/10**18*eth_value

# Combine dataframe and drop nulls
combined_usd_fees = pd.concat([azuki_usd_fees.reset_index(drop=True),
                               cryptopunks_usd_fees.reset_index(drop=True),
                               BAYC_usd_fees.reset_index(drop=True)], 
                              axis=1
                             ).dropna()
combined_usd_fees.columns = ['azuki_fees', 'cryptopunks_fees','BAYC_fees']
In [30]:
# Plot for combined figure
combined_fees_fig = px.violin(combined_usd_fees, height = 700, title = "Value in USD for fees paid in NFT Sales")

# Show Figure
combined_fees_fig.show()

Part Three: Cryptopunks¶

@dockingbay24

Beeple Art

In [28]:
# create variable for api key  for etherscan
ETHERSCAN_API_KEY = os.getenv("ETHERSCAN_API")

#set api url variables for Etherscan call 
cryptopunks_contract = "0xb47e3cd837dDF8e4c57F05d70Ab865de6e193BBB"
etherscan_url = "https://api.etherscan.io/api"
module = "?module=contract"
action = "&action=getsourcecode"
address ="&address=" + cryptopunks_contract  
etherscan_key = "&apikey=" + ETHERSCAN_API_KEY
In [29]:
# Set API call string
url_cryptopunks_contract_details = etherscan_url+module+action+address+etherscan_key

# Get results from API call
cryptopunk_contract_details= requests.get(url_cryptopunks_contract_details).json()

Etherscan Cryptopunks Transactions¶

In [30]:
# Set api url variables for Etherscan call 
transaction_hash = "0x15f8e5ea1079d9a0bb04a4c58ae5fe7654b5b2b4463375ff7ffb490aa0032f3a" #replace with trans_hash
etherscan_url = "https://api.etherscan.io/api"
module = "?module=transaction"
action = "&action=getstatus"
address ="&&txhash=" + transaction_hash 
etherscan_key = "&apikey=" + ETHERSCAN_API_KEY
In [31]:
# Set API call string
url_cryptopunks_transactions = etherscan_url+module+action+address+etherscan_key
In [32]:
# Get results from API call
cryptopunk_transaction=requests.get(url_cryptopunks_transactions).json()

a. Wrapped Cryptopunks¶

In [33]:
# Append url for our api
url = "https://api.covalenthq.com/v1"
chain_id = "/1"    #TEMP is it always chain1 for most part?
option = "/nft_market/collection"
# Add search queries to api url
contract_address = "/0xb7f7f6c52f2e2fdb1963eab30438024864c313f6"   #Do we want to compare other contracts
currency = "/?quote-currency=USD"
format_output = "&format=JSON"
date_from ="&from=2022-01-25"
date_to = "&to=2022-04-25"
covalent_api_key = "&key=" + api_key

url_nft_market_cap_detail = url + chain_id + option + contract_address + currency + format_output + date_from + date_to  + covalent_api_key
In [34]:
#set API call string
url_nft_market_cap_detail = url + chain_id + option + contract_address + currency + format_output + date_from + date_to  + covalent_api_key
In [35]:
#get results from API call
nft_market_cap = requests.get(url_nft_market_cap_detail).json()
In [36]:
#set data into a dataframe
nft_market_cap_df = pd.DataFrame(nft_market_cap['data']['items'])
In [37]:
#display head and tail of df
display(nft_market_cap_df.head())
chain_id collection_name collection_address collection_ticker_symbol opening_date volume_wei_day volume_quote_day average_volume_wei_day average_volume_quote_day unique_token_ids_sold_count_day ... fourth_nft_image_token_id fourth_nft_image fourth_nft_image_256 fourth_nft_image_512 fourth_nft_image_1024 fifth_nft_image_token_id fifth_nft_image fifth_nft_image_256 fifth_nft_image_512 fifth_nft_image_1024
0 1 Wrapped Cryptopunks 0xb7f7f6c52f2e2fdb1963eab30438024864c313f6 WPUNKS 2022-03-30 201500000000000000000 687428.9000 201500000000000000000 687428.9000 1 ... 51 https://wrappedpunks.com:3000/images/punks/51.png https://image-proxy.svc.prod.covalenthq.com/25... https://image-proxy.svc.prod.covalenthq.com/51... https://image-proxy.svc.prod.covalenthq.com/10... 60 https://wrappedpunks.com:3000/images/punks/60.png https://image-proxy.svc.prod.covalenthq.com/25... https://image-proxy.svc.prod.covalenthq.com/51... https://image-proxy.svc.prod.covalenthq.com/10...
1 1 Wrapped Cryptopunks 0xb7f7f6c52f2e2fdb1963eab30438024864c313f6 WPUNKS 2022-02-03 250000000000000000 664.4058 250000000000000000 664.4058 1 ... 51 https://wrappedpunks.com:3000/images/punks/51.png https://image-proxy.svc.prod.covalenthq.com/25... https://image-proxy.svc.prod.covalenthq.com/51... https://image-proxy.svc.prod.covalenthq.com/10... 60 https://wrappedpunks.com:3000/images/punks/60.png https://image-proxy.svc.prod.covalenthq.com/25... https://image-proxy.svc.prod.covalenthq.com/51... https://image-proxy.svc.prod.covalenthq.com/10...

2 rows × 39 columns

In [38]:
#TEMP descibe data
nft_market_cap_df.describe()
Out[38]:
chain_id volume_quote_day average_volume_quote_day unique_token_ids_sold_count_day floor_price_quote_7d gas_quote_rate_day
count 2.0 2.000000 2.000000 2.0 2.000000 2.000000
mean 1.0 344046.652900 344046.652900 1.0 344046.652900 3034.590450
std 0.0 485615.830927 485615.830927 0.0 485615.830927 533.112056
min 1.0 664.405800 664.405800 1.0 664.405800 2657.623300
25% 1.0 172355.529350 172355.529350 1.0 172355.529350 2846.106875
50% 1.0 344046.652900 344046.652900 1.0 344046.652900 3034.590450
75% 1.0 515737.776450 515737.776450 1.0 515737.776450 3223.074025
max 1.0 687428.900000 687428.900000 1.0 687428.900000 3411.557600
In [39]:
#TEMP list columns of df
nft_market_cap_df.columns
Out[39]:
Index(['chain_id', 'collection_name', 'collection_address',
       'collection_ticker_symbol', 'opening_date', 'volume_wei_day',
       'volume_quote_day', 'average_volume_wei_day',
       'average_volume_quote_day', 'unique_token_ids_sold_count_day',
       'floor_price_wei_7d', 'floor_price_quote_7d', 'gas_quote_rate_day',
       'quote_currency', 'first_nft_image_token_id', 'first_nft_image',
       'first_nft_image_256', 'first_nft_image_512', 'first_nft_image_1024',
       'second_nft_image_token_id', 'second_nft_image', 'second_nft_image_256',
       'second_nft_image_512', 'second_nft_image_1024',
       'third_nft_image_token_id', 'third_nft_image', 'third_nft_image_256',
       'third_nft_image_512', 'third_nft_image_1024',
       'fourth_nft_image_token_id', 'fourth_nft_image', 'fourth_nft_image_256',
       'fourth_nft_image_512', 'fourth_nft_image_1024',
       'fifth_nft_image_token_id', 'fifth_nft_image', 'fifth_nft_image_256',
       'fifth_nft_image_512', 'fifth_nft_image_1024'],
      dtype='object')
In [40]:
# Create a new data frame for graphing volume, drop un-needed columns
market_cap_df_graph = nft_market_cap_df[["opening_date","volume_quote_day"]].copy()
In [41]:
# TEMP display market_cap_df_graph
market_cap_df_graph
Out[41]:
opening_date volume_quote_day
0 2022-03-30 687428.9000
1 2022-02-03 664.4058
In [42]:
# Graph dataframe for analysis
from bokeh.models.formatters import NumeralTickFormatter
formatter = NumeralTickFormatter(format="0,0")

market_cap_df_graph.hvplot.bar(
    x='opening_date', 
    y='volume_quote_day',
    xlabel='Opening Date',
    ylabel='Volume',
    rot=90,
    title='Volume Quote Per Day - 0xb7f7f6c52f2e2fdb1963eab30438024864c313f6',
    height= 600,
    width = 2000
).opts(
  yformatter=formatter
)
Out[42]:

b. Punks not wrapped¶

In [43]:
# Set variables
url = "https://api.covalenthq.com/v1"
chain_id = "/1"    #TEMP is it always chain1 for most part?
option = "/nft_market/collection"

# Add search queries to api url
contract_address2 = "/0xb47e3cd837dDF8e4c57F05d70Ab865de6e193BBB"
currency = "/?quote-currency=USD"
format_output = "&format=JSON"
date_from ="&from=2022-01-25"
date_to = "&to=2022-04-25"
covalent_api_key = "&key=" + api_key

# Append url for our api
url_nft_market_cap_detail2 = url + chain_id + option + contract_address2 + currency + format_output + date_from + date_to  + covalent_api_key
In [44]:
#set API call string
url_nft_market_cap_detail2 = url + chain_id + option + contract_address2 + currency + format_output + date_from + date_to  + covalent_api_key 
#get results from API call
nft_market_cap2 = requests.get(url_nft_market_cap_detail2).json()
In [45]:
#set data into a dataframe
nft_market_cap_df2 = pd.DataFrame(nft_market_cap2['data']['items'])
In [46]:
#set data into a dataframe
nft_market_cap_df2 = pd.DataFrame(nft_market_cap2['data']['items'])
#create a new data frame for graphing volume, drop un-needed columns
market_cap_df_graph_2 = nft_market_cap_df2[["opening_date","volume_quote_day"]].copy()
In [47]:
#Graph dataframe for analysis
from bokeh.models.formatters import NumeralTickFormatter
formatter = NumeralTickFormatter(format="0,0")

market_cap_df_graph_2.hvplot.bar(
    x='opening_date', 
    y='volume_quote_day',
    xlabel='Opening Date',
    ylabel='Volume',
    rot=90,
    title='Volume Quote Per Day - Cryptopunks',
    height= 600,
    width = 1000
).opts(
  yformatter=formatter
)
Out[47]:
In [48]:
from bokeh.models.formatters import NumeralTickFormatter
formatter = NumeralTickFormatter(format="0,0")
graph2 = market_cap_df_graph_2.hvplot.bar(
    x='opening_date', 
    y='volume_quote_day',
    xlabel='Opening Date',
    ylabel='Volume',
    rot=90,
    label='0xb47e3cd837dDF8e4c57F05d70Ab865de6e193BBB',
    height= 600,
    width = 1000
).opts(
  yformatter=formatter
) 

graph1  = market_cap_df_graph.hvplot.bar(
    x='opening_date', 
    y='volume_quote_day',
    xlabel='Opening Date',
    ylabel='Volume',
    rot=90,
    label='0xb7f7f6c52f2e2fdb1963eab30438024864c313f6',
    height= 600,
    width = 1000
).opts(
  yformatter=formatter
)
In [49]:
graph2 * graph1
Out[49]:
In [50]:
market_cap_df_graph['Token'] = '0xb7f7f6c52f2e2fdb1963eab30438024864c313f6'
market_cap_df_graph_2['Token'] = '0xb47e3cd837dDF8e4c57F05d70Ab865de6e193BBB'

# combine dataframes into a single df
combined_df = pd.concat([market_cap_df_graph, market_cap_df_graph_2], join="outer", ignore_index=False)

c. Combined Token Graph¶

In [51]:
# Combined Token graph
from bokeh.models.formatters import NumeralTickFormatter
formatter = NumeralTickFormatter(format="0,0")
combined_df.hvplot.scatter(
    x='opening_date', 
    y='volume_quote_day',
    xlabel='Date',
    ylabel='Volume',
    rot=90,
    label='Combined Analysis',
    by='Token',
    attr_labels=False,
    height= 600,
    width = 1000
).opts(
  yformatter=formatter
)
Out[51]:

d. import historical data¶

In [52]:
# Read in all cryptopunkowners
cryptopunk_owners_path = Path("./Resources_punks/2022-05_all_cryptopunk_owners.csv")

# Read in top20 sales, by ether value
top20_sales_path = Path("./Resources_punks/top20_sales_by_ether_value.csv")
In [53]:
#import into dataframes
cryptopunk_owners_df = pd.read_csv(cryptopunk_owners_path, index_col="#", parse_dates=True, infer_datetime_format=True)
top20_sales_df = pd.read_csv(top20_sales_path, index_col="Punk", parse_dates=True, infer_datetime_format=True)

# Display tem values for dataframes
display(cryptopunk_owners_df.head())
display(top20_sales_df.head())
Account OpenSea / ENS Number Owned last Active
#
1 0xb7f7f6c52f2e2 WrappedCryptoPu 428 7 hours ago
2 0xa858ddc0445d8 NaN 423 1 month ago
3 0xa25803ab86a32 wilcox.eth 238 28 days ago
4 0xb88f61e6fbda8 NaN 215 11 months ago
5 0x577ebc5de943e NaN 165 5 days ago
Ether EtherValueUSD_M Date
Punk
5822 8000 23.70 02/12/22
7804 4200 7.57 03/11/21
3100 4200 7.58 03/11/22
5577 2500 7.70 02/09/22
4156 2500 10.26 12/09/21
In [54]:
#plot top20 sales by Punk based on Ether
top20_sales_df.hvplot.scatter(
    x='EtherValueUSD_M', 
    y='Ether',
    xlabel='Ether value in USD Millions',
    ylabel='Ether',
    rot=90,
    label='Top 20 Sales By Ether',
    by='Punk',
    height= 600,
    width = 1000
).opts(
    bgcolor='lightgray',
   #fontsize={'title': 16, 'labels': 14, 'xticks': 6, 'yticks': 12}
)
Out[54]:
In [55]:
# Plot top20 sales by Punk based on Ether
top20_sales_df.hvplot.table(
    x='EtherValueUSD_M', 
    y='Ether',
    xlabel='Ether value in USD Millions',
    ylabel='Ether',
    rot=90,
    label='Top 20 Sales By Ether',
    by='Punk',
    height= 600,
    width = 1000
).opts(
    bgcolor='lightgray',
   #fontsize={'title': 16, 'labels': 14, 'xticks': 6, 'yticks': 12}
)
Out[55]:
In [56]:
#validate dataframe total owned is 10,000
cryptopunk_total_assets = cryptopunk_owners_df['Number Owned'].sum()
cryptopunk_total_assets
Out[56]:
10000
In [57]:
#find mean number of NFTs owned per owner
cryptopunk_owners_mean = cryptopunk_owners_df['Number Owned'].mean()
cryptopunk_owners_mean
Out[57]:
2.914602156805596
In [58]:
#top20 asset owners
top20_cryptopunk_owners = cryptopunk_owners_df.head(20)
top20_cryptopunk_owners
Out[58]:
Account OpenSea / ENS Number Owned last Active
#
1 0xb7f7f6c52f2e2 WrappedCryptoPu 428 7 hours ago
2 0xa858ddc0445d8 NaN 423 1 month ago
3 0xa25803ab86a32 wilcox.eth 238 28 days ago
4 0xb88f61e6fbda8 NaN 215 11 months ago
5 0x577ebc5de943e NaN 165 5 days ago
6 0x69021ae876958 sov.eth 146 6 months ago
7 0x26f744711ee9e NaN 141 4 years ago
8 0x4084df8bf74ba NaN 98 NaN
9 0x269616d549d7e NaN 96 9 days ago
10 0x31a5ff62a1b2c NaN 93 1 month ago
11 0x7174039818a41 NaN 89 3 years ago
12 0xcc7c335f3365a NaN 87 13 days ago
13 0x51688cd36c188 NaN 79 6 days ago
14 0x810fdbc7e5cfe NaN 77 13 hours ago
15 0xf5a4ba515dd36 NaN 75 1 month ago
16 0xcffc336e6d019 NaN 74 2 months ago
17 0x6f4a2d3a4f47f NaN 70 9 days ago
18 0x062c5432107e3 NaN 68 3 months ago
19 0x7760e0243ca9b NaN 66 3 years ago
20 0xdde8df9a7dc9f Kenney 66 2 months ago
In [59]:
#total amount of assets owned by the top20 owners
top20_owners_assets = cryptopunk_owners_df['Number Owned'].head(20).sum()
perc_top20_owners_assets = top20_owners_assets /cryptopunk_total_assets *100
print(f"The Top 20 owners own {top20_owners_assets} NFTs, which is {perc_top20_owners_assets:.2f}% of total assets.")

The Top 20 owners own 2794 NFTs, which is 27.94% of total assets.
In [60]:
#total amount of assets owned by the top100 owners
top100_owners_assets = cryptopunk_owners_df['Number Owned'].head(100).sum()
perc_top100_owners_assets = top100_owners_assets /cryptopunk_total_assets *100
print(f"The Top 100 owners own {top100_owners_assets} NFTs, which is {perc_top100_owners_assets:.2f}% of total assets.")

The Top 100 owners own 4705 NFTs, which is 47.05% of total assets.
In [61]:
#total amount of assets owned by the top20 owners
bot20_owners_assets = cryptopunk_owners_df['Number Owned'].tail(20).sum()
perc_bot20_owners_assets = bot20_owners_assets /cryptopunk_total_assets *100
print(f"The Bottom 20 owners own {bot20_owners_assets} NFTs, which is {perc_bot20_owners_assets:.2f}% of total assets.")

The Bottom 20 owners own 20 NFTs, which is 0.20% of total assets.